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Ben Hodges, Associate Professor, UT Austin Center for Research in Water Resources

The following is a guest post authored by Ben Hodges,  Associate Professor, University of Texas at Austin Center for Research in Water Resources.

Although many of us are sweltering in record-breaking heat, a recent Wall Street Journal story about the race to shore up aging, damaged levee systems along the Mississipi River reminds us that flood season is just around the corner.  And according to the U.S. Army Corps of Engineers, the multi-billion dollar restoration won’t be done by spring.

Deciding where to begin is a complex task.  But with the right mix of technology and expertise, engineers could have a snapshot of how a river and its tributaries will behave in flood situations and other extreme weather conditions, allowing them to prioritize levee restoration efforts according to which areas are at highest risk of flooding, and when that’s likely to happen.

As part of a Research collaboration with the University of Texas at Austin, IBM has come up with a new technology that could provide such a snapshot.  YouTube Preview Image

This new flood prediction technology can simulate tens of thousands of river branches at a time and could scale further to predict the behavior of millions of branches simultaneously. By coupling analytics software with advanced weather simulation models, such as IBM’s Deep Thunder, municipalities and disaster response teams could make emergency plans and pinpoint potential flood areas on a river.

Floods are the most common natural disaster in the United States, but traditional flood prediction methods are focused only on the main stems of the largest rivers – overlooking extensive tributary networks where flooding actually starts, and where flash floods threaten lives and property.

As a testing ground, the team is presently applying the model to predict the entire 230 mile-long Guadalupe River and over 9,000 miles of tributaries in Texas. In a single hour the system can currently generate up to 100 hours of river behavior.

By combining IBM’s complex system modeling with UT Austin’s research into river physics, we’ve developed new ways to look at an old problem. Unlike previous methods, the IBM approach scales-up for massive networks and has the potential to simulate millions of river miles at once. With the use of river sensors integrated into web-based information systems, we can take this model even further.

In addition to flood prediction, a similar system could be used for irrigation management, helping to create equitable irrigation plans and ensure compliance with habitat conservation efforts. The models could allow managers to evaluate multiple “what if” scenarios to create better plans for handling both droughts and water surplus.

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